stress {SWIM}

Stressing Random Variables

Description

Provides weights on simulated scenarios from a baseline stochastic model, such that stressed random variables fulfil given probabilistic constraints (e.g. specified values for risk measures), under the new scenario weights. Scenario weights are selected by constrained minimisation of the relative entropy to the baseline model.

Usage

stress(
  type = c("VaR", "VaR ES", "mean", "mean sd", "moment", "prob", "user"),
  x,
  ...
)

Arguments

type

Type of stress, one of "VaR", "VaR ES", "mean", "mean sd", "moment", "prob", "user".

x

A vector, matrix or data frame containing realisations of random variables. Columns of x correspond to random variables; OR
A SWIM object, where x corresponds to the underlying data of the SWIM object.

...

Arguments to be passed on, depending on type.

Value

An object of class SWIM, see SWIM for details.

Author(s)

Silvana M. Pesenti

References

Pesenti SM, Millossovich P, Tsanakas A (2019). “Reverse sensitivity testing: What does it take to break the model?” European Journal of Operational Research, 274(2), 654–670.

Pesenti S BAMPTA (2020). “Scenario Weights for Importance Measurement (SWIM) - An R package for sensitivity analysis.” Annals of Actuarial Science 15.2 (2021): 458-483. Available at SSRN: https://www.ssrn.com/abstract=3515274.

Csiszar I (1975). “I-divergence geometry of probability distributions and minimization problems.” The Annals of Probability, 146–158.

See Also

Other stress functions: stress_HARA_RM_w(), stress_RM_mean_sd_w(), stress_RM_w(), stress_VaR_ES(), stress_VaR(), stress_mean_sd_w(), stress_mean_sd(), stress_mean_w(), stress_mean(), stress_moment(), stress_prob(), stress_user(), stress_wass()

Examples

set.seed(0)
x <- as.data.frame(cbind(
  "normal" = rnorm(1000), 
  "gamma" = rgamma(1000, shape = 2)))
res <- stress(type = "VaR", x = x, 
  alpha = 0.9, q_ratio = 1.05)
summary(res)